Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Methods Inf Med. 2023 Dec;62(5-06):174-182. doi: 10.1055/s-0043-1771378. Epub 2023 Jul 24.
Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful.
The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective.
Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim.
The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%.
The results show that the objectives defined were possible to solve reasonably.
不良事件在医疗保健中很常见。在精神科治疗中，与其他医学专业相比，患者受伤的赔偿索赔似乎较少。精神科患者伤害索赔中最常见的类型包括诊断缺陷、无法预防的自杀或被视为不必要或有害的强制性治疗。
目的是研究是否可以根据赔偿索赔的精神科评估形成与患者伤害类型相关的不同类别，并基于这些类别进行机器学习分类。此外，赔偿索赔的阳性和阴性决定的二进制分类是另一个目标。
应用人工智能的机器学习方法将芬兰精神科专家对患者伤害赔偿索赔的评估分为六个不同类别，称为类别。此外，对相同数据进行了另一种分类，分为两类，以测试是否可以根据其已知决定，即接受或拒绝赔偿索赔，对数据案例进行分类。
前者的分类任务在分离不同类别方面产生了相对较好的分类结果。相比之下，后者则更加复杂。然而，通过在分类之前的预处理阶段生成人工数据案例，可以提高这两个任务的分类准确性。这种预处理方法将随机森林分类法的六类分类准确率提高到 88%，将二进制分类的准确率提高到 89%。
结果表明，定义的目标是可以合理解决的。
